First-generation expert systems are known in the database processing arts as production systems where the knowledge base and inference engine are disjointed. Second-generation expert systems are improved in the art to include a rudimentary learning capability, which may be implemented by the interpretation of grids or by user query. Third-generation expert systems are further improved to provide for rule base learning through the use of deductive and inductive processes.
Conventional analysis of information includes manually combing through vast databases and unstructured text/reports. An alternative method would be to use a database computer language such as structured query language (SQL) to perform directed mining operations. This approach is not fully general, is difficult and costly to maintain, and does not provide a capability for linking database events. Moreover, such database mining is less than optimal at rendering conclusions and probabilities, as rendering conclusions may require association of ostensively random events. An example of event-related activity for detection would be detection based on a previously acquired signature of the activity.
Conventional expert systems require that all knowledge be hand-tailored and manually checked for validity and consistency. In particular, conventional alternatives are either not creative or do not reason using symbolic knowledge; i.e., computing with words. Conventional analysis of information includes manually combing through vast databases and unstructured text/reports. For purposes of data analysis, a given concept may be broadly defined or defined within a particular context. This may or may not have a direct correspondence to a general definition of the concept, but relates to a specific aspect of the concept. Therefore, substantial human analogical reasoning is required.
U.S. Pat. No. 7,047,226, to Stuart H. Rubin, titled “System and Method for Knowledge Amplification Employing Structured Expert Randomization” describes a Type 1 Knowledge Amplification Employing Structured Expert Randomization (KASER) engine. U.S. Non-provisional patent application Ser. No. 12/390,633, filed Feb. 23, 2009, by Stuart H. Rubin, titled, “System and Method for Type 2 KASER (Knowledge Amplification by Structured Expert Randomization)” describes the general concept of a Type 2 KASER engine. The Type 1 KASER is described as allowing the user to supply declarative knowledge in the form of a semantic tree using single inheritance. In a Type 1 KASER, words and phrases are entered into the system by an operator by means of, for example, pull-down menus. In this manner, semantically identical concepts (e.g., Hello and Hi) may be entered with equivalent syntax by the operator to avoid diluting the learning efficiency of the KASER. The Type 2 KASER is described as automatically inducing this semantic tree, and having means for performing randomization and set operations on the property trees that are acquired by way of, for example, database query and user-interaction. Distinct syntax may be logically equated by the operator to yield the equivalent normalized semantics.
A need exists for an expert system architecture that may automatically expand the rule base without the concomitant data input burden associated with error correction needed to optimize expert system performance. An expert system that includes learning means for acquiring a rule system that functions as a larger virtual rule system with reduced error probability has, until now, been unavailable in the art. These unresolved problems and deficiencies are clearly felt in the art and are solved by the present subject matter in the manner described below.